{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:53.138113Z",
     "start_time": "2020-04-22T07:36:51.908964Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# 要求tf版本在1.4以上，2.0以上默认开启eager_execution模式\n",
    "tf.enable_eager_execution()  \n",
    "\n",
    "# 让程序自动的选择最优的线程并行个数\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据集\n",
    "\n",
    "下载数据，[Kaggle地址](https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据分为train与test两个文件夹，其中train文件夹中的文件名含标签，test测试集中的不含标签，具体测试成绩需要提交到Kaggle才能看到。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:54.548287Z",
     "start_time": "2020-04-22T07:36:54.536512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ssd/pyr/CatVSDog/train\n",
      "/ssd/pyr/CatVSDog/test\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "data_root_orig = \"/ssd/pyr/CatVSDog\"\n",
    "data_root = pathlib.Path(data_root_orig)\n",
    "\n",
    "for item in data_root.iterdir():\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取训练集与测试集目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:55.120957Z",
     "start_time": "2020-04-22T07:36:55.107561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ssd/pyr/CatVSDog/train\n",
      "/ssd/pyr/CatVSDog/test\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "train_image_paths = os.path.join(data_root_orig, 'train')\n",
    "test_image_paths = os.path.join(data_root_orig, 'test')\n",
    "print(train_image_paths)\n",
    "print(test_image_paths)\n",
    "\n",
    "train_image_paths = pathlib.Path(train_image_paths)\n",
    "test_image_paths = pathlib.Path(test_image_paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取所有训练图像路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:55.787875Z",
     "start_time": "2020-04-22T07:36:55.605800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ssd/pyr/CatVSDog/train/cat/cat.6263.jpg\n",
      "/ssd/pyr/CatVSDog/train/dog/dog.9881.jpg\n",
      "/ssd/pyr/CatVSDog/train/cat/cat.3650.jpg\n",
      "/ssd/pyr/CatVSDog/train/dog/dog.8253.jpg\n",
      "/ssd/pyr/CatVSDog/train/cat/cat.7418.jpg\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(611)\n",
    "\n",
    "all_train_images = list(train_image_paths.glob('*/*'))\n",
    "\n",
    "# 将WindowsPath中的Path单独取出\n",
    "all_train_images = [str(path) for path in all_train_images]\n",
    "\n",
    "for i in range(5):\n",
    "    print(random.choice(all_train_images))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分别获取猫、狗的训练集目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:56.176792Z",
     "start_time": "2020-04-22T07:36:56.140939Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ssd/pyr/CatVSDog/train/cat/cat.647.jpg\n",
      "/ssd/pyr/CatVSDog/train/cat/cat.10619.jpg\n",
      "/ssd/pyr/CatVSDog/train/cat/cat.1087.jpg\n",
      "/ssd/pyr/CatVSDog/train/cat/cat.10674.jpg\n",
      "/ssd/pyr/CatVSDog/train/cat/cat.4139.jpg\n",
      "/ssd/pyr/CatVSDog/train/cat/cat.11317.jpg\n"
     ]
    }
   ],
   "source": [
    "train_cat = os.path.join(train_image_paths, 'cat')\n",
    "train_dog = os.path.join(train_image_paths, 'dog')\n",
    "\n",
    "train_cat = pathlib.Path(train_cat)\n",
    "train_dog = pathlib.Path(train_dog)\n",
    "\n",
    "for i,item in enumerate(train_cat.iterdir()):\n",
    "    if i > 5:\n",
    "        break\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:56.726539Z",
     "start_time": "2020-04-22T07:36:56.347163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "random.seed(611)\n",
    "\n",
    "for i in range(4):\n",
    "    image_path = random.choice(all_train_images)\n",
    "    img = cv2.imread(image_path)\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.xticks([])  # 指定x轴的刻度显示（不显示）\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)  # 不绘制网格\n",
    "    image_rel = pathlib.Path(image_path).relative_to(data_root)\n",
    "    plt.xlabel(image_rel)\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 确定图片标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:57.129226Z",
     "start_time": "2020-04-22T07:36:57.115950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cat', 'dog']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = sorted(item.name for item in train_image_paths.glob('*/') if item.is_dir())\n",
    "label_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为每个标签分配索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:57.926067Z",
     "start_time": "2020-04-22T07:36:57.585005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cat': 0, 'dog': 1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_to_index = dict((name, index) for index, name in enumerate(label_names))\n",
    "label_to_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:58.195751Z",
     "start_time": "2020-04-22T07:36:57.999817Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 labels indices:  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "all_train_labels = [label_to_index[pathlib.Path(path).parent.name]\n",
    "                    for path in all_train_images]\n",
    "\n",
    "print(\"First 10 labels indices: \", all_train_labels[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载和格式化图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:36:58.269199Z",
     "start_time": "2020-04-22T07:36:58.265651Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_and_preprocess_image(path):\n",
    "    image = tf.io.read_file(path)  # 读取原始数据\n",
    "    return preprocess_image(image)\n",
    "\n",
    "\n",
    "def preprocess_image(image):\n",
    "    image = tf.image.decode_jpeg(image, channels=3)  # 将数据解码为张量tensor\n",
    "    image = tf.image.resize(image, [192, 192])  # 根据模型调整大小\n",
    "    image /= 255.0  # normalize to [0,1] range\n",
    "\n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试图片读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:38:16.764976Z",
     "start_time": "2020-04-22T07:38:16.529825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = all_train_images[0]\n",
    "label = all_train_labels[0]\n",
    "\n",
    "plt.imshow(load_and_preprocess_image(image_path))\n",
    "plt.grid(False)\n",
    "plt.title(label_names[label].title())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将路径与标签打包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:42:47.714826Z",
     "start_time": "2020-04-22T07:42:47.495735Z"
    }
   },
   "outputs": [],
   "source": [
    "ds = tf.data.Dataset.from_tensor_slices((all_train_images, all_train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:43:01.660491Z",
     "start_time": "2020-04-22T07:43:01.377397Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DatasetV1Adapter shapes: ((192, 192, 3), ()), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 元祖元素被分别解压缩，并映射到函数的参数中\n",
    "def load_and_preprocess_from_path_label(path, label):\n",
    "    return load_and_preprocess_image(path), label\n",
    "\n",
    "image_label_ds = ds.map(load_and_preprocess_from_path_label)\n",
    "image_label_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设置batch准备训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:45:40.663794Z",
     "start_time": "2020-04-22T07:45:40.540700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DatasetV1Adapter shapes: ((?, 192, 192, 3), (?,)), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 16\n",
    "image_count = len(all_train_images)\n",
    "ds = image_label_ds.shuffle(buffer_size=image_count)  # 打乱数据\n",
    "ds = ds.repeat()  # 使数据不断重复\n",
    "ds = ds.batch(BATCH_SIZE)  # 每次batch取多少数据\n",
    "\n",
    "ds = ds.prefetch(buffer_size=AUTOTUNE)  # 模型训练时，数据集在后台取得batch\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 搭建自定义模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 搭建模型 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T08:15:47.587205Z",
     "start_time": "2020-04-22T08:15:47.443635Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 192, 192, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 190, 190, 32)      896       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 188, 188, 64)      18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 94, 94, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 94, 94, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 565504)            0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               72384640  \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 72,404,290\n",
      "Trainable params: 72,404,290\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "# 清除之前的模型\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "inputs = Input(shape=(192, 192, 3))\n",
    "x = Conv2D(32, kernel_size=(3, 3), activation='relu')(inputs)\n",
    "x = Conv2D(64, kernel_size=(3, 3), activation='relu')(x)\n",
    "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "x = Dropout(0.25)(x)\n",
    "x = Flatten()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(2, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=predictions)\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用手写体数字识别中的简单模型\n",
    "一轮训练后，训练集正确率为60%，耗时1分钟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T08:16:56.844150Z",
     "start_time": "2020-04-22T08:15:50.185803Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 1563.0 steps\n",
      "1563/1563 [==============================] - 67s 43ms/step - loss: 0.7266 - acc: 0.5995\n"
     ]
    }
   ],
   "source": [
    "steps_per_epoch = tf.math.ceil(len(all_train_images) / BATCH_SIZE).numpy()\n",
    "epochs = 1\n",
    "# model.fit(ds, epochs=1, steps_per_epoch=steps_per_epoch)  # 训练一轮\n",
    "history = model.fit(ds,\n",
    "                    epochs=1,\n",
    "                    verbose=1,\n",
    "                    steps_per_epoch=steps_per_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 迁移学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:51:52.377656Z",
     "start_time": "2020-04-22T07:51:50.026133Z"
    }
   },
   "outputs": [],
   "source": [
    "# 清除之前的模型\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "resNet50 = tf.keras.applications.ResNet50(input_shape=(192, 192, 3), include_top=False)\n",
    "resNet50.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查对应模型的输入输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:50:20.258315Z",
     "start_time": "2020-04-22T07:50:20.253917Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function preprocess_input in module keras_applications.imagenet_utils:\n",
      "\n",
      "preprocess_input(x, data_format=None, mode='caffe', **kwargs)\n",
      "    Preprocesses a tensor or Numpy array encoding a batch of images.\n",
      "    \n",
      "    # Arguments\n",
      "        x: Input Numpy or symbolic tensor, 3D or 4D.\n",
      "            The preprocessed data is written over the input data\n",
      "            if the data types are compatible. To avoid this\n",
      "            behaviour, `numpy.copy(x)` can be used.\n",
      "        data_format: Data format of the image tensor/array.\n",
      "        mode: One of \"caffe\", \"tf\" or \"torch\".\n",
      "            - caffe: will convert the images from RGB to BGR,\n",
      "                then will zero-center each color channel with\n",
      "                respect to the ImageNet dataset,\n",
      "                without scaling.\n",
      "            - tf: will scale pixels between -1 and 1,\n",
      "                sample-wise.\n",
      "            - torch: will scale pixels between 0 and 1 and then\n",
      "                will normalize each channel with respect to the\n",
      "                ImageNet dataset.\n",
      "    \n",
      "    # Returns\n",
      "        Preprocessed tensor or Numpy array.\n",
      "    \n",
      "    # Raises\n",
      "        ValueError: In case of unknown `data_format` argument.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import keras_applications\n",
    "\n",
    "help(keras_applications.resnet50.preprocess_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "启动一个batch的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:51:48.468052Z",
     "start_time": "2020-04-22T07:51:10.919630Z"
    }
   },
   "outputs": [],
   "source": [
    "image_batch, label_batch = next(iter(ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "传递一个batch的图片给这个模型，查看结果，预期输出(BATCH_SIZE, 6, 6, 2048)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:52:12.724517Z",
     "start_time": "2020-04-22T07:52:02.306442Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 6, 6, 2048)\n"
     ]
    }
   ],
   "source": [
    "feature_map_batch = resNet50(image_batch)\n",
    "print(feature_map_batch.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建模型，在模型最后添加简单的空间池化与全连接层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:52:49.617564Z",
     "start_time": "2020-04-22T07:52:48.312982Z"
    }
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    resNet50,\n",
    "    tf.keras.layers.GlobalAveragePooling2D(),\n",
    "    tf.keras.layers.Dense(len(label_names), activation='softmax')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "试验模型，相当于进行一次前向传播"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:53:04.391233Z",
     "start_time": "2020-04-22T07:53:04.140155Z"
    }
   },
   "outputs": [],
   "source": [
    "logit_batch = model(image_batch).numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查最大值、最小值与输出形状符合预期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:53:28.639003Z",
     "start_time": "2020-04-22T07:53:28.626620Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min logit: 0.23164885\n",
      "max logit: 0.76835114\n",
      "Shape: (16, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"min logit:\", logit_batch.min())\n",
    "print(\"max logit:\", logit_batch.max())\n",
    "print(\"Shape:\", logit_batch.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "编译模型，仅训练全连接层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:55:06.951912Z",
     "start_time": "2020-04-22T07:55:06.847475Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "resnet50 (Model)             (None, 6, 6, 2048)        23587712  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 4098      \n",
      "=================================================================\n",
      "Total params: 23,591,810\n",
      "Trainable params: 4,098\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n",
      "trainable_variables: 2\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model.summary()\n",
    "print(\"trainable_variables:\", len(model.trainable_variables))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算steps_per_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:57:44.195918Z",
     "start_time": "2020-04-22T07:57:44.118206Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1563.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps_per_epoch = tf.math.ceil(len(all_train_images) / BATCH_SIZE).numpy()\n",
    "steps_per_epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练模型\n",
    "\n",
    "采用预训练的resnet50模型，同样1分钟训练时间，训练1轮，正确率达到了94%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T07:59:47.852473Z",
     "start_time": "2020-04-22T07:58:46.523412Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 1563.0 steps\n",
      "1563/1563 [==============================] - 61s 39ms/step - loss: 0.1462 - acc: 0.9432\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f7191dc1e10>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(ds, epochs=1, steps_per_epoch=steps_per_epoch)  # 训练一轮"
   ]
  }
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